Senior Data Engineer

About Ryewalk

At Ryewalk, we go beyond solving problems - we engineer smart, scalable solutions that drive business success.

• Salesforce Experts – We don't just implement CRM solutions; we tailor Salesforce to fit your business, ensuring seamless adoption and maximum impact.
• Cloud Strategists – We design and build cloud architectures in AWS that are robust, scalable, and ready for the future.
• Tech Trailblazers – When technology challenges stall progress, we step in, streamline, and deliver. No jargon, no fluff—just outcomes that matter.
• Decision Scientists – We don't just crunch numbers; we turn data into insight and insight into action—combining analytics, modeling, and decision science to drive smarter business decisions.

If it's a complex tech challenge, we're the team that makes it work.

About the Role

We're looking for a Senior Data Engineer to build and own the data pipelines, warehouse, and reporting layers that power analytics and decision-making for our clients.

This is a hands-on role with real ownership. You'll take data from source systems all the way to dashboards - designing pipelines that are reliable and easy to monitor, shaping raw data into models people trust, and putting clear, self-serve reporting in the hands of business teams. Along the way, you'll help shape how we build and grow our data engineering practice.

The role centers on three things:

• Pipeline builder — you design and run reliable, scalable pipelines that bring data in from everywhere it lives.
• Warehouse & modeling owner — you turn raw data into trusted, analysis-ready models that balance performance, cost, and usability.
• Insight enabler — you build the dashboards and data layers that let business teams answer their own questions with confidence.

If you enjoy owning things end to end, working closely with the people who use your data, and building platforms that just work, we'd love to talk.

Key Responsibilities

Data Pipelines & Ingestion

• Source and ingest data end-to-end from diverse systems — databases, APIs, flat files, and third-party feeds — building pipelines that are reliable, scalable, and easy to monitor.
• Design and maintain ETL/ELT workflows that clean, transform, and validate raw data into trusted, analysis-ready datasets.
• Support both batch and near-real-time data needs, with monitoring, alerting, and recovery paths so issues are caught and fixed early.

Build automated data quality checks — validation, profiling, and simple anomaly detection — so the numbers stay trustworthy as sources evolve.

Data Warehouse & Modeling

• Contribute to the overall data architecture — how data is ingested, stored, modeled, and served — making pragmatic, cost-aware technology choices.
• Build and optimize data models within the warehouse, balancing performance, storage cost, and usability.
• Develop a clean, tested, version-controlled transformation layer that others can understand and build on.
• Help establish good engineering practices — naming conventions, environments, code review, and documentation — as the data platform grows.
• Handle data responsibly — access controls, retention, and privacy-aware practices (e.g., GDPR) — built into the platform from the start rather than bolted on.

Reporting, Self-Serve Analytics & Stakeholders

• Enable self-serve reporting and analytics by developing dashboards and data layers that business teams can use with confidence in the numbers.
• Manage stakeholder relationships across business and engineering — gathering requirements, explaining trade-offs simply, and flagging data risks early.
• Keep documentation current — data definitions, lineage, and runbooks — so knowledge is shared, not siloed.

You'll thrive in this role if you

• Take ownership naturally — you like seeing things through from idea to production.
• Are pragmatic — you pick the simplest solution that works and improve it over time.
• Care about the people using your data — trust in the numbers matters as much as the pipeline behind them.
• Set things up well by default — clear naming, good docs, and tidy code, without being asked.

What we're looking for

Must-Have:

• 3+ years of hands-on data engineering experience building and running production data pipelines.
• Strong SQL and solid Python — the everyday tools of this role.
• Experience with ETL/ELT and orchestration tools such as Airflow, dbt, Glue, or Data Factory.
• Hands-on experience with a cloud data warehouse such as Snowflake, Redshift, BigQuery, or ClickHouse, including data modeling for analytics.
• Experience working on at least one major cloud platform (AWS, Azure, or GCP).
• Experience building dashboards and reports with a BI tool such as Tableau, Power BI, or Looker Studio.
• Comfort working with both batch and near-real-time data.
• Clear communication — you can gather requirements, explain technical trade-offs in plain language, and raise risks early.
• Good engineering habits: Git, testing, and documentation as part of your normal workflow.
• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience.

Nice-to-Have:

• Business Intelligence / Business Analyst experience — requirements gathering, KPI definition, and partnering closely with business teams. A strong plus.
• Experience with streaming or CDC technologies (e.g., Kafka, Kinesis, Fivetran, Debezium).
• Experience with high-volume event, IoT, or time-series data.
• Experience with Infrastructure-as-Code (e.g., Terraform, CloudFormation) and containerization/orchestration tools like Docker and Kubernetes.
• Distributed data processing (e.g., Spark) or lakehouse architectures (e.g., Databricks).
• Scripting knowledge (e.g., Shell scripting, Bash) to automate data workflows and tasks.
• Experience supporting data science or ML teams with clean, well-modeled data.
• Exposure to data governance, cataloging, or data quality frameworks (e.g., Great Expectations).
• Experience in a consulting environment or working with clients across time zones.

If you don't tick every box but this role sounds exciting, we'd still love to hear from you.

What success looks like

• First 30 days: You've built a clear picture of the data landscape — sources, systems, and stakeholders — and shipped your first improvements to how data flows.
• Within 3 months: Pipelines and warehouse models are running reliably, business teams are answering their own questions from dashboards they trust, and the platform is documented and easy to build on.

What Makes You a Great Fit

Problem-Solver Mindset - You enjoy tackling challenges and finding innovative solutions

Excellent Troubleshooting Abilities - You can quickly diagnose and resolve technical issues under pressure

Curiosity & Continuous Learning - You stay updated with the latest Data Engineering trends and technologies

Ownership & Accountability - You take responsibility and drive projects to completion

Collaboration & Communication - You work well across teams and can explain technical concepts clearly

Adaptability - You thrive in a dynamic environment and embrace change

Proactive Approach - You anticipate challenges, take initiative, and suggest improvements before issues arise

Why Ryewalk

Impact from Day One - As a startup, we move fast, make bold decisions, and your work truly matters. No red tape - just pure problem-solving and execution.

Wear Multiple Hats (If You Want To!) - Love coding but also want to dabble in architecture Curious about Consulting At Ryewalk, you'll have the freedom to explore beyond your job title.

Work With the Best - Join a team of smart, passionate, and hands-on tech pros who thrive on collaboration, innovation, and challenging the status quo.

Grow at Startup Speed - Unlike rigid corporate hierarchies, here you get to learn, lead, and level up - fast. 

Hybrid-Friendly & Flexible - We trust you to deliver without micromanagement. (Yes, really!)

Solve Real Problems - We work with clients who come to us with big, complex tech challenges - and we love cracking them. If you enjoy making a real impact, you'll fit right in.

Culture That Feels Like Home - Expect collaborative brainstorming, inside jokes, and an environment where everyone has a voice.